Run ’download_data.Rmd` First!
options(na.action = "na.fail")
library(tidyverse)
library(dplyr)
source("./helper__dredge_functions.R")
model_average_richness <- function(data) {
model_average(percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + urban + realm + number_of_checklists_scaled + (1|city_name), data, 4)
}
create_city_dataset <- function(mixed_model) {
city_coeefs <- ranef(mixed_model)$city_name
city_coeefs <- cbind(city_name = rownames(city_coeefs), city_coeefs)
rownames(city_coeefs) <- NULL
names(city_coeefs) <- c('name', 'response')
left_join(city_coeefs, city_data)
}
| Merlin: Difference in richness |
merlin_richness_result <- model_average_richness(merlin)
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
merlin_richness_result
Call:
model.avg(object = dredge_result, subset = delta < aic_delta)
Component model call:
lmer(formula = percentage_of_regional_pool_present ~ <8 unique rhs>, data = data)
Component models:
df logLik AICc delta weight
1/2/3/4/5/6/7/8/9/10/11 18 -24617.98 49272.05 0.00 0.24
1/2/3/4/5/6/7/8/10/11 17 -24619.11 49272.29 0.24 0.21
1/2/4/5/6/7/8/9/10/11 17 -24619.43 49272.93 0.89 0.15
1/2/4/5/6/7/8/10/11 16 -24620.49 49273.04 0.99 0.14
1/2/3/4/5/6/7/8/9/10/11/12 19 -24618.08 49274.25 2.20 0.08
1/2/3/4/5/6/7/8/10/11/12 18 -24619.09 49274.26 2.22 0.08
1/2/4/5/6/7/8/10/11/12 17 -24620.47 49275.02 2.97 0.05
1/2/4/5/6/7/8/9/10/11/12 18 -24619.52 49275.12 3.07 0.05
Term codes:
closed_forest cultivated elevation_delta_scaled herbaceous_vegetation herbaceous_wetland mean_elevation_scaled
1 2 3 4 5 6
number_of_checklists_scaled open_forest permanent_water realm shrubs urban
7 8 9 10 11 12
Model-averaged coefficients:
(full average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 14.09316 0.88391 0.88404 15.942 < 2e-16 ***
closed_forest 3.89893 0.70680 0.70690 5.516 < 2e-16 ***
cultivated 11.37400 0.98130 0.98144 11.589 < 2e-16 ***
elevation_delta_scaled 0.58915 0.82679 0.82687 0.713 0.4761
herbaceous_vegetation 9.88097 1.08890 1.08906 9.073 < 2e-16 ***
herbaceous_wetland 12.35171 3.13811 3.13856 3.935 8.30e-05 ***
mean_elevation_scaled -4.60340 1.81547 1.81571 2.535 0.0112 *
number_of_checklists_scaled 20.82193 0.64287 0.64296 32.384 < 2e-16 ***
open_forest 12.96359 0.98655 0.98669 13.138 < 2e-16 ***
permanent_water -0.36087 0.59918 0.59923 0.602 0.5470
realmAfrotropic 3.42908 1.91360 1.91387 1.792 0.0732 .
realmAustralasia -3.14597 3.13851 3.13896 1.002 0.3162
realmIndomalayan -1.55901 1.21340 1.21357 1.285 0.1989
realmNearctic -5.99756 1.41243 1.41263 4.246 2.18e-05 ***
realmNeotropic -2.12324 1.42796 1.42817 1.487 0.1371
shrubs 6.29980 1.45822 1.45842 4.320 1.56e-05 ***
urban 0.04047 0.18754 0.18756 0.216 0.8292
(conditional average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 14.0932 0.8839 0.8840 15.942 < 2e-16 ***
closed_forest 3.8989 0.7068 0.7069 5.516 < 2e-16 ***
cultivated 11.3740 0.9813 0.9814 11.589 < 2e-16 ***
elevation_delta_scaled 0.9802 0.8683 0.8685 1.129 0.2591
herbaceous_vegetation 9.8810 1.0889 1.0891 9.073 < 2e-16 ***
herbaceous_wetland 12.3517 3.1381 3.1386 3.935 8.30e-05 ***
mean_elevation_scaled -4.6034 1.8155 1.8157 2.535 0.0112 *
number_of_checklists_scaled 20.8219 0.6429 0.6430 32.384 < 2e-16 ***
open_forest 12.9636 0.9866 0.9867 13.138 < 2e-16 ***
permanent_water -0.6993 0.6775 0.6776 1.032 0.3021
realmAfrotropic 3.4291 1.9136 1.9139 1.792 0.0732 .
realmAustralasia -3.1460 3.1385 3.1390 1.002 0.3162
realmIndomalayan -1.5590 1.2134 1.2136 1.285 0.1989
realmNearctic -5.9976 1.4124 1.4126 4.246 2.18e-05 ***
realmNeotropic -2.1232 1.4280 1.4282 1.487 0.1371
shrubs 6.2998 1.4582 1.4584 4.320 1.56e-05 ***
urban 0.1554 0.3423 0.3424 0.454 0.6499
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
merlin_mixed_model <- lmer(percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1|city_name), merlin)
r.squaredGLMM(merlin_mixed_model)
R2m R2c
[1,] 0.1614687 0.6330958
merlin_fixed_model <- lm(percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled, merlin)
anova(merlin_mixed_model, merlin_fixed_model)
refitting model(s) with ML (instead of REML)
Data: merlin
Models:
merlin_fixed_model: percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled
merlin_mixed_model: percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1 | city_name)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
merlin_fixed_model 17 50580 50700 -25273 50546
merlin_mixed_model 18 49305 49432 -24634 49269 1277.7 1 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova(merlin_mixed_model, lm(percentage_of_regional_pool_present ~ city_name, merlin))
refitting model(s) with ML (instead of REML)
Data: merlin
Models:
merlin_mixed_model: percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1 | city_name)
lm(percentage_of_regional_pool_present ~ city_name, merlin): percentage_of_regional_pool_present ~ city_name
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
merlin_mixed_model 18 49305 49432 -24634 49269
lm(percentage_of_regional_pool_present ~ city_name, merlin) 138 50605 51576 -25164 50329 0 120 1
merlin_richness_sum <- model_summary('merlin', 'richness', merlin_richness_result)
merlin_richness_sum
merlin_city_response <- create_city_dataset(merlin_mixed_model)
Joining, by = "name"
merlin_city_response[order(merlin_city_response$response), c('name', 'response')]
write_csv(merlin_city_response[, c('name', 'response')], 'percentage_of_regional_richness__output__merlin_city_richness_intercept.csv')
summary(merlin_mixed_model)
Linear mixed model fit by REML ['lmerMod']
Formula: percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation +
herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1 | city_name)
Data: merlin
REML criterion at convergence: 49236
Scaled residuals:
Min 1Q Median 3Q Max
-4.0484 -0.6453 -0.0591 0.5795 6.2777
Random effects:
Groups Name Variance Std.Dev.
city_name (Intercept) 24.59 4.959
Residual 19.13 4.374
Number of obs: 8443, groups: city_name, 137
Fixed effects:
Estimate Std. Error t value
(Intercept) 14.1438 0.8719 16.222
mean_elevation_scaled -5.0263 1.8278 -2.750
elevation_delta_scaled 1.0054 0.8685 1.158
closed_forest 3.8365 0.6942 5.526
cultivated 11.3259 0.9672 11.710
herbaceous_vegetation 9.8109 1.0795 9.088
herbaceous_wetland 12.4718 3.1352 3.978
open_forest 12.8555 0.9867 13.029
permanent_water -0.7345 0.6589 -1.115
shrubs 6.2175 1.4540 4.276
realmAfrotropic 3.5255 1.9121 1.844
realmAustralasia -3.1666 3.1348 -1.010
realmIndomalayan -1.5482 1.2120 -1.277
realmNearctic -5.9691 1.4107 -4.231
realmNeotropic -2.0731 1.4264 -1.453
number_of_checklists_scaled 20.8490 0.6433 32.411
Correlation matrix not shown by default, as p = 16 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need it
merlin_richness_plot <- merlin_richness_sum[merlin_richness_sum$model == 'full',]
merlin_richness_plot$explanatory <- factor(merlin_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
merlin_richness_plot$type <- 'Proportion of landcover\n5 km around site'
merlin_richness_plot$type[merlin_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
merlin_richness_plot$type[merlin_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
merlin_richness_plot$type[merlin_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'
ggplot(merlin_richness_plot, aes(y=explanatory, x=richness_merlin_estimate, colour = type)) +
geom_line() +
geom_point()+
geom_errorbar(aes(xmin=richness_merlin_estimate-richness_merlin_error, xmax=richness_merlin_estimate+richness_merlin_error), width=.2,
position=position_dodge(0.05)) +
scale_y_discrete(
limits = rev(levels(merlin_richness_plot$explanatory)),
labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
theme_bw() +
geom_vline(xintercept=0, linetype="dotted") +
guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional richness\n± Standard Error') + ylab('Predictor') +
theme(legend.justification = "top")
ggsave('percentage_of_regional_pool__output__merlin_richness_result.jpg')
Saving 7.29 x 4.51 in image

| Birdlife: Difference in richness |
bl_richness_result <- model_average_richness(birdlife)
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
bl_richness_result
Call:
model.avg(object = dredge_result, subset = delta < aic_delta)
Component model call:
lmer(formula = percentage_of_regional_pool_present ~ <5 unique rhs>, data = data)
Component models:
df logLik AICc delta weight
1/2/4/5/6/7/8/9/10/11 17 -25622.28 51278.63 0.00 0.32
1/2/3/4/5/6/7/8/9/10/11 18 -25621.28 51278.64 0.00 0.32
1/2/4/5/6/7/8/9/10/11/12 18 -25621.95 51279.97 1.34 0.16
1/2/3/4/5/6/7/8/9/10/11/12 19 -25620.96 51280.01 1.38 0.16
1/2/4/5/6/7/8/10/11/12 17 -25624.23 51282.53 3.89 0.05
Term codes:
closed_forest cultivated elevation_delta_scaled herbaceous_vegetation herbaceous_wetland mean_elevation_scaled
1 2 3 4 5 6
number_of_checklists_scaled open_forest permanent_water realm shrubs urban
7 8 9 10 11 12
Model-averaged coefficients:
(full average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 12.3727 0.6656 0.6657 18.586 < 2e-16 ***
closed_forest 4.6122 0.8136 0.8137 5.668 < 2e-16 ***
cultivated 11.3601 1.1041 1.1043 10.287 < 2e-16 ***
herbaceous_vegetation 11.6039 1.2364 1.2366 9.384 < 2e-16 ***
herbaceous_wetland 12.0168 3.5407 3.5412 3.393 0.00069 ***
mean_elevation_scaled -3.7033 1.5005 1.5007 2.468 0.01360 *
number_of_checklists_scaled 25.0703 0.7297 0.7298 34.353 < 2e-16 ***
open_forest 16.1070 1.1164 1.1165 14.426 < 2e-16 ***
permanent_water -1.5753 0.8385 0.8386 1.879 0.06031 .
realmAfrotropic -1.4162 1.4153 1.4155 1.000 0.31709
realmAustralasia -3.2249 2.1301 2.1304 1.514 0.13009
realmIndomalayan -4.3603 0.8615 0.8616 5.061 4.00e-07 ***
realmNearctic -2.6755 0.9471 0.9473 2.824 0.00474 **
realmNeotropic -4.1732 1.0243 1.0245 4.074 4.63e-05 ***
shrubs 9.0283 1.6084 1.6086 5.613 < 2e-16 ***
elevation_delta_scaled 0.2427 0.6903 0.6904 0.351 0.72523
urban 0.1323 0.3002 0.3002 0.441 0.65943
(conditional average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 12.3727 0.6656 0.6657 18.586 < 2e-16 ***
closed_forest 4.6122 0.8136 0.8137 5.668 < 2e-16 ***
cultivated 11.3601 1.1041 1.1043 10.287 < 2e-16 ***
herbaceous_vegetation 11.6039 1.2364 1.2366 9.384 < 2e-16 ***
herbaceous_wetland 12.0168 3.5407 3.5412 3.393 0.00069 ***
mean_elevation_scaled -3.7033 1.5005 1.5007 2.468 0.01360 *
number_of_checklists_scaled 25.0703 0.7297 0.7298 34.353 < 2e-16 ***
open_forest 16.1070 1.1164 1.1165 14.426 < 2e-16 ***
permanent_water -1.6499 0.7830 0.7832 2.107 0.03514 *
realmAfrotropic -1.4162 1.4153 1.4155 1.000 0.31709
realmAustralasia -3.2249 2.1301 2.1304 1.514 0.13009
realmIndomalayan -4.3603 0.8615 0.8616 5.061 4.00e-07 ***
realmNearctic -2.6755 0.9471 0.9473 2.824 0.00474 **
realmNeotropic -4.1732 1.0243 1.0245 4.074 4.63e-05 ***
shrubs 9.0283 1.6084 1.6086 5.613 < 2e-16 ***
elevation_delta_scaled 0.5102 0.9303 0.9304 0.548 0.58343
urban 0.3606 0.4040 0.4041 0.892 0.37217
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
birdlife_mixed_model <- lmer(percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + shrubs + realm + (1|city_name), birdlife)
r.squaredGLMM(birdlife_mixed_model)
R2m R2c
[1,] 0.1072897 0.3305271
summary(birdlife_mixed_model)
Linear mixed model fit by REML ['lmerMod']
Formula: percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland +
open_forest + shrubs + realm + (1 | city_name)
Data: birdlife
REML criterion at convergence: 52351.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.2243 -0.6782 -0.0908 0.6152 4.9342
Random effects:
Groups Name Variance Std.Dev.
city_name (Intercept) 9.381 3.063
Residual 28.132 5.304
Number of obs: 8443, groups: city_name, 137
Fixed effects:
Estimate Std. Error t value
(Intercept) 13.2793 0.6152 21.584
mean_elevation_scaled -3.5910 1.4355 -2.502
closed_forest 3.6458 0.8310 4.387
cultivated 11.7687 1.1288 10.426
herbaceous_vegetation 10.1846 1.2886 7.904
herbaceous_wetland 9.8345 3.7552 2.619
open_forest 15.7218 1.1651 13.495
shrubs 9.3424 1.6824 5.553
realmAfrotropic -1.5413 1.4110 -1.092
realmAustralasia -3.0458 2.0803 -1.464
realmIndomalayan -4.2258 0.8522 -4.958
realmNearctic -1.7182 0.9236 -1.860
realmNeotropic -4.0139 1.0135 -3.960
Correlation matrix not shown by default, as p = 13 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need it
birdlife_fixed_model <- lm(percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + shrubs + realm, birdlife)
anova(birdlife_mixed_model, birdlife_fixed_model)
refitting model(s) with ML (instead of REML)
Data: birdlife
Models:
birdlife_fixed_model: percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + shrubs + realm
birdlife_mixed_model: percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + shrubs + realm + (1 | city_name)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
birdlife_fixed_model 14 53110 53208 -26541 53082
birdlife_mixed_model 15 52408 52514 -26189 52378 703.39 1 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(birdlife_fixed_model)
Call:
lm(formula = percentage_of_regional_pool_present ~ mean_elevation_scaled +
closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland +
open_forest + shrubs + realm, data = birdlife)
Residuals:
Min 1Q Median 3Q Max
-16.7661 -4.0084 -0.5982 3.3769 27.5192
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.5614 0.2319 62.795 < 2e-16 ***
mean_elevation_scaled -3.7600 0.5323 -7.064 1.75e-12 ***
closed_forest 2.0352 0.7227 2.816 0.00487 **
cultivated 16.0866 0.9808 16.401 < 2e-16 ***
herbaceous_vegetation 9.2569 1.0864 8.521 < 2e-16 ***
herbaceous_wetland 0.2377 3.5321 0.067 0.94636
open_forest 14.3239 1.0159 14.100 < 2e-16 ***
shrubs 6.7982 1.1102 6.124 9.56e-10 ***
realmAfrotropic -2.1892 0.7785 -2.812 0.00493 **
realmAustralasia -4.3652 0.3482 -12.537 < 2e-16 ***
realmIndomalayan -5.7999 0.3142 -18.460 < 2e-16 ***
realmNearctic -2.5121 0.2285 -10.995 < 2e-16 ***
realmNeotropic -5.2385 0.3279 -15.978 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.615 on 8430 degrees of freedom
Multiple R-squared: 0.1424, Adjusted R-squared: 0.1411
F-statistic: 116.6 on 12 and 8430 DF, p-value: < 2.2e-16
birdlife_city_response <- create_city_dataset(birdlife_mixed_model)
Joining, by = "name"
birdlife_city_response[order(birdlife_city_response$response), c('name', 'response')]
write_csv(birdlife_city_response[, c('name', 'response')], 'percentage_of_regional_richness__output__birdlife_city_richness_intercept.csv')
bl_richness_sum <- model_summary('birdlife', 'richness', bl_richness_result)
bl_richness_sum
summary(birdlife_mixed_model)
Linear mixed model fit by REML ['lmerMod']
Formula: percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland +
open_forest + shrubs + realm + (1 | city_name)
Data: birdlife
REML criterion at convergence: 52351.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.2243 -0.6782 -0.0908 0.6152 4.9342
Random effects:
Groups Name Variance Std.Dev.
city_name (Intercept) 9.381 3.063
Residual 28.132 5.304
Number of obs: 8443, groups: city_name, 137
Fixed effects:
Estimate Std. Error t value
(Intercept) 13.2793 0.6152 21.584
mean_elevation_scaled -3.5910 1.4355 -2.502
closed_forest 3.6458 0.8310 4.387
cultivated 11.7687 1.1288 10.426
herbaceous_vegetation 10.1846 1.2886 7.904
herbaceous_wetland 9.8345 3.7552 2.619
open_forest 15.7218 1.1651 13.495
shrubs 9.3424 1.6824 5.553
realmAfrotropic -1.5413 1.4110 -1.092
realmAustralasia -3.0458 2.0803 -1.464
realmIndomalayan -4.2258 0.8522 -4.958
realmNearctic -1.7182 0.9236 -1.860
realmNeotropic -4.0139 1.0135 -3.960
Correlation matrix not shown by default, as p = 13 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need it
birdlife_richness_plot <- bl_richness_sum[bl_richness_sum$model == 'full',]
birdlife_richness_plot$explanatory <- factor(birdlife_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlife_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlife_richness_plot$type[birdlife_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlife_richness_plot$type[birdlife_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlife_richness_plot$type[birdlife_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'
birdlife_plot = ggplot(birdlife_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) +
geom_line() +
geom_point()+
geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
position=position_dodge(0.05)) +
scale_y_discrete(
limits = rev(levels(birdlife_richness_plot$explanatory)),
labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
theme_bw() +
geom_vline(xintercept=0, linetype="dotted") +
guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
theme(legend.justification = "top")
birdlife_plot
ggsave('percentage_of_regional_pool__output__birdlife_richness_result.jpg')
Saving 7.29 x 4.51 in image

| Both: Difference in richness |
both_richness_result <- model_average_richness(both)
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
Warning: Model failed to converge with max|grad| = 0.00314894 (tol = 0.002, component 1)
both_richness_result
Call:
model.avg(object = dredge_result, subset = delta < aic_delta)
Component model call:
lmer(formula = percentage_of_regional_pool_present ~ <7 unique rhs>, data = data)
Component models:
df logLik AICc delta weight
1/2/3/4/5/6/7/8/9/10/11 18 -26535.43 53106.94 0.00 0.35
1/2/4/5/6/7/8/9/10/11 17 -26536.96 53107.99 1.06 0.20
1/2/3/4/5/6/7/8/9/10/11/12 19 -26535.19 53108.46 1.53 0.16
1/2/4/5/6/7/8/9/10/11/12 18 -26536.70 53109.48 2.54 0.10
1/2/3/4/5/6/7/8/10/11 17 -26537.95 53109.98 3.05 0.08
1/2/3/4/5/6/7/8/10/11/12 18 -26537.13 53110.34 3.41 0.06
1/2/4/5/6/7/8/10/11 16 -26539.40 53110.85 3.92 0.05
Term codes:
closed_forest cultivated elevation_delta_scaled herbaceous_vegetation herbaceous_wetland mean_elevation_scaled
1 2 3 4 5 6
number_of_checklists_scaled open_forest permanent_water realm shrubs urban
7 8 9 10 11 12
Model-averaged coefficients:
(full average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 16.76949 0.93298 0.93311 17.972 < 2e-16 ***
closed_forest 5.23725 0.89790 0.89803 5.832 < 2e-16 ***
cultivated 13.56656 1.23804 1.23822 10.957 < 2e-16 ***
elevation_delta_scaled 0.70796 1.00687 1.00697 0.703 0.48202
herbaceous_vegetation 12.60120 1.37494 1.37514 9.164 < 2e-16 ***
herbaceous_wetland 16.36089 3.95058 3.95114 4.141 3.46e-05 ***
mean_elevation_scaled -5.47279 2.05661 2.05689 2.661 0.00780 **
number_of_checklists_scaled 26.72527 0.81075 0.81087 32.959 < 2e-16 ***
open_forest 17.44821 1.24768 1.24785 13.983 < 2e-16 ***
permanent_water -1.20865 0.97092 0.97101 1.245 0.21323
realmAfrotropic 1.82107 2.01065 2.01093 0.906 0.36516
realmAustralasia -0.58699 3.20839 3.20885 0.183 0.85485
realmIndomalayan -3.30686 1.25716 1.25734 2.630 0.00854 **
realmNearctic -6.40401 1.43543 1.43563 4.461 8.20e-06 ***
realmNeotropic -4.08496 1.48660 1.48681 2.747 0.00601 **
shrubs 9.75278 1.82036 1.82062 5.357 1.00e-07 ***
urban 0.09439 0.28888 0.28892 0.327 0.74389
(conditional average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 16.7695 0.9330 0.9331 17.972 < 2e-16 ***
closed_forest 5.2372 0.8979 0.8980 5.832 < 2e-16 ***
cultivated 13.5666 1.2380 1.2382 10.957 < 2e-16 ***
elevation_delta_scaled 1.0912 1.0698 1.0699 1.020 0.30776
herbaceous_vegetation 12.6012 1.3749 1.3751 9.164 < 2e-16 ***
herbaceous_wetland 16.3609 3.9506 3.9511 4.141 3.46e-05 ***
mean_elevation_scaled -5.4728 2.0566 2.0569 2.661 0.00780 **
number_of_checklists_scaled 26.7253 0.8108 0.8109 32.959 < 2e-16 ***
open_forest 17.4482 1.2477 1.2479 13.983 < 2e-16 ***
permanent_water -1.4886 0.8628 0.8629 1.725 0.08451 .
realmAfrotropic 1.8211 2.0106 2.0109 0.906 0.36516
realmAustralasia -0.5870 3.2084 3.2089 0.183 0.85485
realmIndomalayan -3.3069 1.2572 1.2573 2.630 0.00854 **
realmNearctic -6.4040 1.4354 1.4356 4.461 8.20e-06 ***
realmNeotropic -4.0850 1.4866 1.4868 2.747 0.00601 **
shrubs 9.7528 1.8204 1.8206 5.357 1.00e-07 ***
urban 0.2924 0.4479 0.4480 0.653 0.51389
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
both_mixed_model <- lmer(percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1|city_name), both)
r.squaredGLMM(both_mixed_model)
R2m R2c
[1,] 0.1688506 0.5417001
both_fixed_model <- lm(percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled, both)
anova(both_mixed_model, both_fixed_model)
refitting model(s) with ML (instead of REML)
Data: both
Models:
both_fixed_model: percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled
both_mixed_model: percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1 | city_name)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
both_fixed_model 17 54137 54257 -27052 54103
both_mixed_model 18 53144 53271 -26554 53108 994.91 1 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
both_richness_sum <- model_summary('both', 'richness', both_richness_result)
both_richness_sum
both_city_response <- create_city_dataset(both_mixed_model)
Joining, by = "name"
both_city_response[order(both_city_response$response), c('name', 'response')]
write_csv(both_city_response[, c('name', 'response')], 'percentage_of_regional_richness__output__both_city_richness_intercept.csv')
| Either: Difference in richness |
either_richness_result <- model_average_richness(either)
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
either_richness_result
Call:
model.avg(object = dredge_result, subset = delta < aic_delta)
Component model call:
lmer(formula = percentage_of_regional_pool_present ~ <8 unique rhs>, data = data)
Component models:
df logLik AICc delta weight
1/2/4/5/6/7/8/9/10/11 17 -23755.12 47544.31 0.00 0.23
1/2/3/4/5/6/7/8/9/10/11 18 -23754.19 47544.46 0.15 0.22
1/2/4/5/6/7/8/10/11 16 -23756.62 47545.30 0.98 0.14
1/2/3/4/5/6/7/8/10/11 17 -23755.74 47545.55 1.24 0.13
1/2/4/5/6/7/8/9/10/11/12 18 -23755.19 47546.46 2.15 0.08
1/2/3/4/5/6/7/8/9/10/11/12 19 -23754.28 47546.64 2.33 0.07
1/2/4/5/6/7/8/10/11/12 17 -23756.30 47546.67 2.35 0.07
1/2/3/4/5/6/7/8/10/11/12 18 -23755.43 47546.95 2.63 0.06
Term codes:
closed_forest cultivated elevation_delta_scaled herbaceous_vegetation herbaceous_wetland mean_elevation_scaled
1 2 3 4 5 6
number_of_checklists_scaled open_forest permanent_water realm shrubs urban
7 8 9 10 11 12
Model-averaged coefficients:
(full average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 10.79309 0.56100 0.56108 19.236 < 2e-16 ***
closed_forest 3.48703 0.64338 0.64347 5.419 1.0e-07 ***
cultivated 9.95937 0.87880 0.87892 11.331 < 2e-16 ***
herbaceous_vegetation 8.97326 0.98504 0.98518 9.108 < 2e-16 ***
herbaceous_wetland 10.00181 2.83810 2.83851 3.524 0.000426 ***
mean_elevation_scaled -3.43498 1.27445 1.27463 2.695 0.007041 **
number_of_checklists_scaled 19.74517 0.58407 0.58416 33.801 < 2e-16 ***
open_forest 12.13158 0.89205 0.89218 13.598 < 2e-16 ***
permanent_water -0.51126 0.63442 0.63447 0.806 0.420351
realmAfrotropic 0.07749 1.21551 1.21568 0.064 0.949174
realmAustralasia -1.64760 1.86786 1.86813 0.882 0.377802
realmIndomalayan -2.89610 0.74686 0.74697 3.877 0.000106 ***
realmNearctic -3.08571 0.83146 0.83158 3.711 0.000207 ***
realmNeotropic -2.67969 0.88656 0.88669 3.022 0.002510 **
shrubs 6.10974 1.29325 1.29344 4.724 2.3e-06 ***
elevation_delta_scaled 0.26239 0.58738 0.58745 0.447 0.655126
urban 0.07007 0.20080 0.20082 0.349 0.727156
(conditional average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 10.79309 0.56100 0.56108 19.236 < 2e-16 ***
closed_forest 3.48703 0.64338 0.64347 5.419 1.0e-07 ***
cultivated 9.95937 0.87880 0.87892 11.331 < 2e-16 ***
herbaceous_vegetation 8.97326 0.98504 0.98518 9.108 < 2e-16 ***
herbaceous_wetland 10.00181 2.83810 2.83851 3.524 0.000426 ***
mean_elevation_scaled -3.43498 1.27445 1.27463 2.695 0.007041 **
number_of_checklists_scaled 19.74517 0.58407 0.58416 33.801 < 2e-16 ***
open_forest 12.13158 0.89205 0.89218 13.598 < 2e-16 ***
permanent_water -0.85325 0.61637 0.61645 1.384 0.166319
realmAfrotropic 0.07749 1.21551 1.21568 0.064 0.949174
realmAustralasia -1.64760 1.86786 1.86813 0.882 0.377802
realmIndomalayan -2.89610 0.74686 0.74697 3.877 0.000106 ***
realmNearctic -3.08571 0.83146 0.83158 3.711 0.000207 ***
realmNeotropic -2.67969 0.88656 0.88669 3.022 0.002510 **
shrubs 6.10974 1.29325 1.29344 4.724 2.3e-06 ***
elevation_delta_scaled 0.55247 0.75246 0.75257 0.734 0.462881
urban 0.24538 0.31334 0.31339 0.783 0.433635
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
either_mixed_model <- lmer(percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1|city_name), either)
r.squaredGLMM(either_mixed_model)
R2m R2c
[1,] 0.1617725 0.4424337
either_fixed_model <- lm(percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled, either)
anova(either_mixed_model, either_fixed_model)
refitting model(s) with ML (instead of REML)
Data: either
Models:
either_fixed_model: percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled
either_mixed_model: percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1 | city_name)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
either_fixed_model 16 48390 48503 -24179 48358
either_mixed_model 17 47567 47687 -23767 47533 824.93 1 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
either_richness_sum <- model_summary('either', 'richness', either_richness_result)
either_richness_sum
either_city_response <- create_city_dataset(either_mixed_model)
Joining, by = "name"
either_city_response[order(either_city_response$response), c('name', 'response')]
write_csv(either_city_response[, c('name', 'response')], 'percentage_of_regional_richness__output__either_city_richness_intercept.csv')
richness_all <- full_join(full_join(merlin_richness_sum, bl_richness_sum), full_join(both_richness_sum, either_richness_sum))
Joining, by = c("explanatory", "model")Joining, by = c("explanatory", "model")Joining, by = c("explanatory", "model")
write_csv(richness_all, "percentage_of_regional_richness__output__richness_result.csv")
richness_all
library(tidyr)
library(ggplot2)
library(ggpubr)
environment <- merlin[,c('closed_forest', 'cultivated', 'herbaceous_vegetation', 'herbaceous_wetland', 'open_forest', 'permanent_water', 'shrubs', 'urban')]
names(environment) <- c('Closed Forest', 'Cultivated', 'Herbaceous Vegetation', 'Herbaceous Wetland', 'Open Forest', 'Permanent Water', 'Shurbs', 'Urban')
landcover <- gather(environment, landcover, proportion)
ggplot(landcover, aes(x = landcover, y = proportion)) + geom_boxplot() + xlab('Landcover Class') + ylab('Proportion in 5 km around sites') +
scale_x_discrete(labels = scales::wrap_format(10)) + theme_bw() + labs(title = "Landcover Proportions")
ggsave("percentage_of_regional_richness__output__som_landcover_proportions.jpg")
Saving 7.29 x 4.51 in image

ggarrange(
ggplot(merlin, aes(y = elevation_delta)) + geom_boxplot() + xlab('Elevation Delta') + ylab('Average Metres in 5 km around sites') +
scale_x_discrete(labels = scales::wrap_format(10)) + theme_bw(),
ggplot(merlin, aes(y = mean_elevation)) + geom_boxplot() + xlab('Mean Elevation') + ylab('Average Metres in 5 km around sites') +
scale_x_discrete(labels = scales::wrap_format(10)) + theme_bw()
)
ggsave("percentage_of_regional_richness__output__som_elevations.jpg")
Saving 7.29 x 4.51 in image

| Birdlife [300 or less checklists]: Difference in
richness |
birdlife300 = birdlife[birdlife$number_of_checklists <= 300,]
nrow(birdlife300)
[1] 7190
blr300_richness_result <- model_average_richness(birdlife300)
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
blr300_richness_result
Call:
model.avg(object = dredge_result, subset = delta < aic_delta)
Component model call:
lmer(formula = percentage_of_regional_pool_present ~ <6 unique rhs>, data = data)
Component models:
df logLik AICc delta weight
1/2/3/4/5/6/7/8/9/10/11/12 19 -21709.04 43456.18 0.00 0.25
1/2/4/5/6/7/8/9/10/11/12 18 -21710.05 43456.19 0.02 0.25
1/2/3/4/5/6/7/8/9/10/11 18 -21710.39 43456.88 0.70 0.18
1/2/4/5/6/7/8/9/10/11 17 -21711.44 43456.97 0.80 0.17
1/2/4/5/6/7/8/10/11/12 17 -21712.27 43458.62 2.44 0.07
1/2/3/4/5/6/7/8/10/11/12 18 -21711.30 43458.69 2.52 0.07
Term codes:
closed_forest cultivated elevation_delta_scaled herbaceous_vegetation herbaceous_wetland mean_elevation_scaled
1 2 3 4 5 6
number_of_checklists_scaled open_forest permanent_water realm shrubs urban
7 8 9 10 11 12
Model-averaged coefficients:
(full average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 11.7790 0.7302 0.7304 16.128 < 2e-16 ***
closed_forest 4.7008 0.8989 0.8990 5.229 2.0e-07 ***
cultivated 12.3055 1.1979 1.1980 10.271 < 2e-16 ***
elevation_delta_scaled 0.2506 0.7329 0.7330 0.342 0.732465
herbaceous_vegetation 11.5511 1.3443 1.3445 8.591 < 2e-16 ***
herbaceous_wetland 13.4647 3.7010 3.7016 3.638 0.000275 ***
mean_elevation_scaled -3.4077 1.5165 1.5168 2.247 0.024661 *
number_of_checklists_scaled 29.3514 1.9470 1.9473 15.073 < 2e-16 ***
open_forest 18.2162 1.1871 1.1873 15.342 < 2e-16 ***
permanent_water -1.5377 1.0807 1.0808 1.423 0.154819
realmAfrotropic -1.3731 1.3897 1.3900 0.988 0.323228
realmAustralasia -3.1636 2.0863 2.0866 1.516 0.129492
realmIndomalayan -4.2550 0.8470 0.8471 5.023 5.0e-07 ***
realmNearctic -2.7905 0.9289 0.9291 3.003 0.002669 **
realmNeotropic -4.1342 1.0078 1.0079 4.102 4.1e-05 ***
shrubs 8.6184 1.6968 1.6971 5.078 4.0e-07 ***
urban 0.4908 0.5013 0.5013 0.979 0.327582
(conditional average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 11.7790 0.7302 0.7304 16.128 < 2e-16 ***
closed_forest 4.7008 0.8989 0.8990 5.229 2.0e-07 ***
cultivated 12.3055 1.1979 1.1980 10.271 < 2e-16 ***
elevation_delta_scaled 0.4973 0.9713 0.9714 0.512 0.608703
herbaceous_vegetation 11.5511 1.3443 1.3445 8.591 < 2e-16 ***
herbaceous_wetland 13.4647 3.7010 3.7016 3.638 0.000275 ***
mean_elevation_scaled -3.4077 1.5165 1.5168 2.247 0.024661 *
number_of_checklists_scaled 29.3514 1.9470 1.9473 15.073 < 2e-16 ***
open_forest 18.2162 1.1871 1.1873 15.342 < 2e-16 ***
permanent_water -1.8020 0.9447 0.9448 1.907 0.056488 .
realmAfrotropic -1.3731 1.3897 1.3900 0.988 0.323228
realmAustralasia -3.1636 2.0863 2.0866 1.516 0.129492
realmIndomalayan -4.2550 0.8470 0.8471 5.023 5.0e-07 ***
realmNearctic -2.7905 0.9289 0.9291 3.003 0.002669 **
realmNeotropic -4.1342 1.0078 1.0079 4.102 4.1e-05 ***
shrubs 8.6184 1.6968 1.6971 5.078 4.0e-07 ***
urban 0.7533 0.4335 0.4335 1.738 0.082294 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
bl300_richness_sum <- model_summary('birdlife', 'richness', blr300_richness_result)
bl300_richness_sum
birdlife300_richness_plot <- bl300_richness_sum[bl300_richness_sum$model == 'full',]
birdlife300_richness_plot$explanatory <- factor(birdlife300_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlife300_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlife300_richness_plot$type[birdlife300_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlife300_richness_plot$type[birdlife300_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlife300_richness_plot$type[birdlife300_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'
birdlife300_plot = ggplot(birdlife300_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) +
geom_line() +
geom_point()+
geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
position=position_dodge(0.05)) +
scale_y_discrete(
limits = rev(levels(birdlife300_richness_plot$explanatory)),
labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
theme_bw() +
geom_vline(xintercept=0, linetype="dotted") +
guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
theme(legend.justification = "top")
birdlife300_plot
ggsave('percentage_of_regional_richness__output__birdlife_richness_300_checklists_or_less_result.jpg')
Saving 7.29 x 4.51 in image

| Birdlife [No Australasia]: Difference in richness |
birdlifeNoAustralia = birdlife[birdlife$realm != 'Australasia',]
nrow(birdlifeNoAustralia)
[1] 8014
blrNoAustralia_richness_result <- model_average_richness(birdlifeNoAustralia)
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
blrNoAustralia_richness_result
Call:
model.avg(object = dredge_result, subset = delta < aic_delta)
Component model call:
lmer(formula = percentage_of_regional_pool_present ~ <4 unique rhs>, data = data)
Component models:
df logLik AICc delta weight
1/2/4/5/6/7/8/9/10/11 16 -24381.60 48795.26 0.00 0.36
1/2/3/4/5/6/7/8/9/10/11 17 -24380.71 48795.49 0.23 0.32
1/2/4/5/6/7/8/9/10/11/12 17 -24381.36 48796.80 1.54 0.17
1/2/3/4/5/6/7/8/9/10/11/12 18 -24380.48 48797.04 1.78 0.15
Term codes:
closed_forest cultivated elevation_delta_scaled herbaceous_vegetation herbaceous_wetland mean_elevation_scaled
1 2 3 4 5 6
number_of_checklists_scaled open_forest permanent_water realm shrubs urban
7 8 9 10 11 12
Model-averaged coefficients:
(full average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 12.47596 0.65663 0.65672 18.997 < 2e-16 ***
closed_forest 4.63809 0.82943 0.82955 5.591 < 2e-16 ***
cultivated 11.60344 1.10345 1.10361 10.514 < 2e-16 ***
herbaceous_vegetation 10.41195 1.27598 1.27618 8.159 < 2e-16 ***
herbaceous_wetland 11.44994 3.58957 3.59011 3.189 0.00143 **
mean_elevation_scaled -3.72351 1.50342 1.50364 2.476 0.01327 *
number_of_checklists_scaled 24.96892 0.74474 0.74485 33.522 < 2e-16 ***
open_forest 15.51587 1.13841 1.13858 13.627 < 2e-16 ***
permanent_water -1.62990 0.78561 0.78572 2.074 0.03804 *
realmAfrotropic -1.37071 1.42023 1.42044 0.965 0.33455
realmIndomalayan -4.40896 0.86422 0.86435 5.101 3.00e-07 ***
realmNearctic -2.63636 0.94943 0.94957 2.776 0.00550 **
realmNeotropic -4.11584 1.02769 1.02785 4.004 6.22e-05 ***
shrubs 9.54960 1.61915 1.61939 5.897 < 2e-16 ***
elevation_delta_scaled 0.10597 0.65561 0.65571 0.162 0.87161
urban 0.08344 0.26011 0.26014 0.321 0.74841
(conditional average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 12.4760 0.6566 0.6567 18.997 < 2e-16 ***
closed_forest 4.6381 0.8294 0.8296 5.591 < 2e-16 ***
cultivated 11.6034 1.1034 1.1036 10.514 < 2e-16 ***
herbaceous_vegetation 10.4120 1.2760 1.2762 8.159 < 2e-16 ***
herbaceous_wetland 11.4499 3.5896 3.5901 3.189 0.00143 **
mean_elevation_scaled -3.7235 1.5034 1.5036 2.476 0.01327 *
number_of_checklists_scaled 24.9689 0.7447 0.7449 33.522 < 2e-16 ***
open_forest 15.5159 1.1384 1.1386 13.627 < 2e-16 ***
permanent_water -1.6299 0.7856 0.7857 2.074 0.03804 *
realmAfrotropic -1.3707 1.4202 1.4204 0.965 0.33455
realmIndomalayan -4.4090 0.8642 0.8644 5.101 3.00e-07 ***
realmNearctic -2.6364 0.9494 0.9496 2.776 0.00550 **
realmNeotropic -4.1158 1.0277 1.0278 4.004 6.22e-05 ***
shrubs 9.5496 1.6191 1.6194 5.897 < 2e-16 ***
elevation_delta_scaled 0.2251 0.9414 0.9416 0.239 0.81103
urban 0.2641 0.4080 0.4080 0.647 0.51748
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
blNoAustralia_richness_sum <- model_summary('birdlife', 'richness', blrNoAustralia_richness_result)
blNoAustralia_richness_sum
birdlifeNoAustralia_richness_plot <- blNoAustralia_richness_sum[blNoAustralia_richness_sum$model == 'full',]
birdlifeNoAustralia_richness_plot$explanatory <- factor(birdlifeNoAustralia_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlifeNoAustralia_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlifeNoAustralia_richness_plot$type[birdlifeNoAustralia_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlifeNoAustralia_richness_plot$type[birdlifeNoAustralia_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlifeNoAustralia_richness_plot$type[birdlifeNoAustralia_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'
birdlifeNoAustralia_plot = ggplot(birdlifeNoAustralia_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) +
geom_line() +
geom_point()+
geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
position=position_dodge(0.05)) +
scale_y_discrete(
limits = rev(levels(birdlifeNoAustralia_richness_plot$explanatory)),
labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
theme_bw() +
geom_vline(xintercept=0, linetype="dotted") +
guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
theme(legend.justification = "top")
birdlifeNoAustralia_plot
ggsave('percentage_of_regional_richness__output__birdlife_richness_no_australia.jpg')
Saving 7.29 x 4.51 in image

| Birdlife [No Afrotropics]: Difference in richness |
birdlifeNoAfrica = birdlife[birdlife$realm != 'Afrotropic',]
nrow(birdlifeNoAfrica)
[1] 8385
blrNoAfrica_richness_result <- model_average_richness(birdlifeNoAfrica)
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
blrNoAfrica_richness_result
Call:
model.avg(object = dredge_result, subset = delta < aic_delta)
Component model call:
lmer(formula = percentage_of_regional_pool_present ~ <4 unique rhs>, data = data)
Component models:
df logLik AICc delta weight
1/2/4/5/6/7/8/9/10/11 16 -25422.24 50876.54 0.00 0.36
1/2/3/4/5/6/7/8/9/10/11 17 -25421.29 50876.65 0.10 0.34
1/2/4/5/6/7/8/9/10/11/12 17 -25422.07 50878.22 1.67 0.16
1/2/3/4/5/6/7/8/9/10/11/12 18 -25421.13 50878.34 1.79 0.15
Term codes:
closed_forest cultivated elevation_delta_scaled herbaceous_vegetation herbaceous_wetland mean_elevation_scaled
1 2 3 4 5 6
number_of_checklists_scaled open_forest permanent_water realm shrubs urban
7 8 9 10 11 12
Model-averaged coefficients:
(full average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 12.39958 0.66378 0.66387 18.678 < 2e-16 ***
closed_forest 4.56883 0.80025 0.80037 5.708 < 2e-16 ***
cultivated 11.11759 1.09826 1.09841 10.121 < 2e-16 ***
herbaceous_vegetation 11.98855 1.24148 1.24165 9.655 < 2e-16 ***
herbaceous_wetland 11.07292 3.54284 3.54335 3.125 0.00178 **
mean_elevation_scaled -3.27277 1.60646 1.60669 2.037 0.04165 *
number_of_checklists_scaled 25.05393 0.72773 0.72784 34.422 < 2e-16 ***
open_forest 16.04934 1.11065 1.11081 14.448 < 2e-16 ***
permanent_water -1.64144 0.77354 0.77365 2.122 0.03386 *
realmAustralasia -3.17949 2.18437 2.18469 1.455 0.14557
realmIndomalayan -4.32224 0.88038 0.88051 4.909 9.00e-07 ***
realmNearctic -2.68378 0.97151 0.97165 2.762 0.00574 **
realmNeotropic -4.26194 1.05094 1.05110 4.055 5.02e-05 ***
shrubs 8.49079 1.61473 1.61496 5.258 1.00e-07 ***
elevation_delta_scaled 0.19823 0.68537 0.68546 0.289 0.77243
urban 0.06957 0.24221 0.24224 0.287 0.77398
(conditional average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 12.3996 0.6638 0.6639 18.678 < 2e-16 ***
closed_forest 4.5688 0.8003 0.8004 5.708 < 2e-16 ***
cultivated 11.1176 1.0983 1.0984 10.121 < 2e-16 ***
herbaceous_vegetation 11.9886 1.2415 1.2417 9.655 < 2e-16 ***
herbaceous_wetland 11.0729 3.5428 3.5434 3.125 0.00178 **
mean_elevation_scaled -3.2728 1.6065 1.6067 2.037 0.04165 *
number_of_checklists_scaled 25.0539 0.7277 0.7278 34.422 < 2e-16 ***
open_forest 16.0493 1.1106 1.1108 14.448 < 2e-16 ***
permanent_water -1.6414 0.7735 0.7737 2.122 0.03386 *
realmAustralasia -3.1795 2.1844 2.1847 1.455 0.14557
realmIndomalayan -4.3222 0.8804 0.8805 4.909 9.00e-07 ***
realmNearctic -2.6838 0.9715 0.9717 2.762 0.00574 **
realmNeotropic -4.2619 1.0509 1.0511 4.055 5.02e-05 ***
shrubs 8.4908 1.6147 1.6150 5.258 1.00e-07 ***
elevation_delta_scaled 0.4076 0.9383 0.9385 0.434 0.66406
urban 0.2308 0.3968 0.3968 0.582 0.56085
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
blNoAfrica_richness_sum = model_summary('birdlife', 'richness', blrNoAfrica_richness_result)
birdlifeNoAfrica_richness_plot <- blNoAfrica_richness_sum[blNoAfrica_richness_sum$model == 'full',]
birdlifeNoAfrica_richness_plot$explanatory <- factor(birdlifeNoAfrica_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlifeNoAfrica_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlifeNoAfrica_richness_plot$type[birdlifeNoAfrica_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlifeNoAfrica_richness_plot$type[birdlifeNoAfrica_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlifeNoAfrica_richness_plot$type[birdlifeNoAfrica_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'
birdlifeNoAfrica_plot = ggplot(birdlifeNoAfrica_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) +
geom_line() +
geom_point()+
geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
position=position_dodge(0.05)) +
scale_y_discrete(
limits = rev(levels(birdlifeNoAfrica_richness_plot$explanatory)),
labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
theme_bw() +
geom_vline(xintercept=0, linetype="dotted") +
guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
theme(legend.justification = "top")
birdlifeNoAfrica_plot
ggsave('percentage_of_regional_richness__output__birdlife_richness_no_africa.jpg')
Saving 7.29 x 4.51 in image

| Birdlife [No Afrotropics and no Australisia]: Difference in
richness |
birdlifeNeitherAfrNorAus = birdlife[birdlife$realm != 'Afrotropic' & birdlife$realm != 'Australasia',]
nrow(birdlifeNeitherAfrNorAus)
[1] 7956
blrNAA_richness_result <- model_average_richness(birdlifeNeitherAfrNorAus)
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
blrNAA_richness_result
Call:
model.avg(object = dredge_result, subset = delta < aic_delta)
Component model call:
lmer(formula = percentage_of_regional_pool_present ~ <4 unique rhs>, data = data)
Component models:
df logLik AICc delta weight
1/2/4/5/6/7/8/9/10/11 15 -24182.21 48394.49 0.00 0.38
1/2/3/4/5/6/7/8/9/10/11 16 -24181.34 48394.74 0.26 0.33
1/2/4/5/6/7/8/9/10/11/12 16 -24182.11 48396.28 1.79 0.15
1/2/3/4/5/6/7/8/9/10/11/12 17 -24181.23 48396.54 2.05 0.14
Term codes:
closed_forest cultivated elevation_delta_scaled herbaceous_vegetation herbaceous_wetland mean_elevation_scaled
1 2 3 4 5 6
number_of_checklists_scaled open_forest permanent_water realm shrubs urban
7 8 9 10 11 12
Model-averaged coefficients:
(full average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 12.47873 0.66382 0.66392 18.795 < 2e-16 ***
closed_forest 4.61632 0.82369 0.82382 5.604 < 2e-16 ***
cultivated 11.39742 1.10640 1.10657 10.300 < 2e-16 ***
herbaceous_vegetation 10.81810 1.28914 1.28933 8.390 < 2e-16 ***
herbaceous_wetland 10.50456 3.59220 3.59275 2.924 0.00346 **
mean_elevation_scaled -3.30266 1.61097 1.61122 2.050 0.04038 *
number_of_checklists_scaled 24.94747 0.74290 0.74301 33.576 < 2e-16 ***
open_forest 15.48966 1.13689 1.13707 13.622 < 2e-16 ***
permanent_water -1.61680 0.77844 0.77855 2.077 0.03783 *
realmIndomalayan -4.37384 0.88300 0.88313 4.953 7.00e-07 ***
realmNearctic -2.64602 0.97363 0.97378 2.717 0.00658 **
realmNeotropic -4.20152 1.05432 1.05448 3.984 6.76e-05 ***
shrubs 9.02543 1.62801 1.62826 5.543 < 2e-16 ***
elevation_delta_scaled 0.05651 0.65242 0.65252 0.087 0.93099
urban 0.04821 0.23246 0.23249 0.207 0.83571
(conditional average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 12.4787 0.6638 0.6639 18.795 < 2e-16 ***
closed_forest 4.6163 0.8237 0.8238 5.604 < 2e-16 ***
cultivated 11.3974 1.1064 1.1066 10.300 < 2e-16 ***
herbaceous_vegetation 10.8181 1.2891 1.2893 8.390 < 2e-16 ***
herbaceous_wetland 10.5046 3.5922 3.5928 2.924 0.00346 **
mean_elevation_scaled -3.3027 1.6110 1.6112 2.050 0.04038 *
number_of_checklists_scaled 24.9475 0.7429 0.7430 33.576 < 2e-16 ***
open_forest 15.4897 1.1369 1.1371 13.622 < 2e-16 ***
permanent_water -1.6168 0.7784 0.7786 2.077 0.03783 *
realmIndomalayan -4.3738 0.8830 0.8831 4.953 7.00e-07 ***
realmNearctic -2.6460 0.9736 0.9738 2.717 0.00658 **
realmNeotropic -4.2015 1.0543 1.0545 3.984 6.76e-05 ***
shrubs 9.0254 1.6280 1.6283 5.543 < 2e-16 ***
elevation_delta_scaled 0.1208 0.9498 0.9499 0.127 0.89882
urban 0.1665 0.4085 0.4086 0.407 0.68367
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
blNAA_richness_sum <- model_summary('birdlife', 'richness', blrNAA_richness_result)
blNAA_richness_sum
birdlifeNAA_richness_plot <- blNAA_richness_sum[blNAA_richness_sum$model == 'full',]
birdlifeNAA_richness_plot$explanatory <- factor(birdlifeNAA_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlifeNAA_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlifeNAA_richness_plot$type[birdlifeNAA_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlifeNAA_richness_plot$type[birdlifeNAA_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlifeNAA_richness_plot$type[birdlifeNAA_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'
birdlifeNAA_plot = ggplot(birdlifeNAA_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) +
geom_line() +
geom_point()+
geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
position=position_dodge(0.05)) +
scale_y_discrete(
limits = rev(levels(birdlifeNAA_richness_plot$explanatory)),
labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
theme_bw() +
geom_vline(xintercept=0, linetype="dotted") +
guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
theme(legend.justification = "top")
birdlifeNAA_plot
ggsave('percentage_of_regional_richness__output__birdlife_richness_no_africa_nor_australisia.jpg')
Saving 7.29 x 4.51 in image

| Birdlife [Urban Only]: Difference in richness |
birdlife$urban_rounded = floor(birdlife$urban * 10) / 10
ggplot(birdlife, aes(x = urban_rounded)) + geom_bar()

birdlifeHigherUrban = birdlife[birdlife$urban > 0.2,]
nrow(birdlifeHigherUrban)
[1] 8262
blrHigherUrban_richness_result <- model_average_richness(birdlifeHigherUrban)
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
Warning: Model failed to converge with max|grad| = 0.00435943 (tol = 0.002, component 1)
blrHigherUrban_richness_result
Call:
model.avg(object = dredge_result, subset = delta < aic_delta)
Component model call:
lmer(formula = percentage_of_regional_pool_present ~ <8 unique rhs>, data = data)
Component models:
df logLik AICc delta weight
1/2/3/4/5/6/7/8/9/10/11 18 -25059.71 50155.49 0.00 0.19
1/2/4/5/6/7/8/9/10/11 17 -25060.76 50155.59 0.10 0.18
1/2/3/4/5/6/7/8/9/10/11/12 19 -25059.12 50156.34 0.84 0.12
1/2/4/5/6/7/8/9/10/11/12 18 -25060.15 50156.39 0.90 0.12
1/2/4/5/6/7/8/10/11/12 17 -25061.29 50156.65 1.16 0.11
1/2/3/4/5/6/7/8/10/11/12 18 -25060.29 50156.65 1.16 0.11
1/2/3/4/5/6/7/8/10/11 17 -25061.47 50157.02 1.53 0.09
1/2/4/5/6/7/8/10/11 16 -25062.49 50157.05 1.55 0.09
Term codes:
closed_forest cultivated elevation_delta_scaled herbaceous_vegetation herbaceous_wetland mean_elevation_scaled
1 2 3 4 5 6
number_of_checklists_scaled open_forest permanent_water realm shrubs urban
7 8 9 10 11 12
Model-averaged coefficients:
(full average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 12.0109 0.6931 0.6931 17.328 < 2e-16 ***
closed_forest 4.8906 0.8534 0.8535 5.730 < 2e-16 ***
cultivated 12.1076 1.1800 1.1801 10.259 < 2e-16 ***
elevation_delta_scaled 0.2824 0.7237 0.7238 0.390 0.696461
herbaceous_vegetation 11.4514 1.2644 1.2646 9.055 < 2e-16 ***
herbaceous_wetland 11.7865 3.6101 3.6106 3.264 0.001097 **
mean_elevation_scaled -3.9688 1.5221 1.5223 2.607 0.009134 **
number_of_checklists_scaled 24.8715 0.7373 0.7374 33.729 < 2e-16 ***
open_forest 17.4451 1.1501 1.1502 15.167 < 2e-16 ***
permanent_water -0.6019 0.8028 0.8029 0.750 0.453499
realmAfrotropic -0.6229 1.4407 1.4409 0.432 0.665529
realmAustralasia -3.0481 2.1240 2.1243 1.435 0.151329
realmIndomalayan -4.2920 0.8641 0.8642 4.967 7e-07 ***
realmNearctic -2.5242 0.9463 0.9464 2.667 0.007648 **
realmNeotropic -3.9439 1.0244 1.0245 3.849 0.000118 ***
shrubs 9.1539 1.6543 1.6545 5.533 < 2e-16 ***
urban 0.2357 0.3837 0.3838 0.614 0.539145
(conditional average)
Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) 12.0109 0.6931 0.6931 17.328 < 2e-16 ***
closed_forest 4.8906 0.8534 0.8535 5.730 < 2e-16 ***
cultivated 12.1076 1.1800 1.1801 10.259 < 2e-16 ***
elevation_delta_scaled 0.5572 0.9383 0.9385 0.594 0.552689
herbaceous_vegetation 11.4514 1.2644 1.2646 9.055 < 2e-16 ***
herbaceous_wetland 11.7865 3.6101 3.6106 3.264 0.001097 **
mean_elevation_scaled -3.9688 1.5221 1.5223 2.607 0.009134 **
number_of_checklists_scaled 24.8715 0.7373 0.7374 33.729 < 2e-16 ***
open_forest 17.4451 1.1501 1.1502 15.167 < 2e-16 ***
permanent_water -0.9816 0.8237 0.8238 1.191 0.233477
realmAfrotropic -0.6229 1.4407 1.4409 0.432 0.665529
realmAustralasia -3.0481 2.1240 2.1243 1.435 0.151329
realmIndomalayan -4.2920 0.8641 0.8642 4.967 7e-07 ***
realmNearctic -2.5242 0.9463 0.9464 2.667 0.007648 **
realmNeotropic -3.9439 1.0244 1.0245 3.849 0.000118 ***
shrubs 9.1539 1.6543 1.6545 5.533 < 2e-16 ***
urban 0.5165 0.4215 0.4216 1.225 0.220493
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
blrHigherUrban_richness_sum <- model_summary('birdlife', 'richness', blrHigherUrban_richness_result)
blrHigherUrban_richness_sum
birdlifeHR_richness_plot <- blrHigherUrban_richness_sum[blrHigherUrban_richness_sum$model == 'full',]
birdlifeHR_richness_plot$explanatory <- factor(birdlifeHR_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlifeHR_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlifeHR_richness_plot$type[birdlifeHR_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlifeHR_richness_plot$type[birdlifeHR_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlifeHR_richness_plot$type[birdlifeHR_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'
birdlifeHR_plot = ggplot(birdlifeHR_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) +
geom_line() +
geom_point()+
geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
position=position_dodge(0.05)) +
scale_y_discrete(
limits = rev(levels(birdlifeHR_richness_plot$explanatory)),
labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
theme_bw() +
geom_vline(xintercept=0, linetype="dotted") +
guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
theme(legend.justification = "top")
birdlifeHR_plot
ggsave('percentage_of_regional_richness__output__birdlife_richness_higher_urban.jpg')
Saving 7.29 x 4.51 in image

| Birdlife [Check pseudoreplication]: Difference in
richness |
locality_samples = read_csv('percentage_of_regional_richness__input__sampled_localities_min_distance_500m.csv')
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
LOCALITY_ID = col_character(),
SAMPLE_ID = col_double()
)
table(locality_samples$SAMPLE_ID)
0 1 2 3 4 5 6 7 8 9
7391 7377 7388 7387 7395 7380 7382 7391 7382 7381
plot_sample = function(sample_id) {
birdlife_sample0 = birdlife[birdlife$locality_id %in% locality_samples$LOCALITY_ID[locality_samples$SAMPLE_ID == sample_id],]
birdlife_sample0_richness_result <- model_average_richness(birdlife_sample0)
birdlife_sample0_richness_summary <- model_summary('birdlife', 'richness', birdlife_sample0_richness_result)
birdlife_sample0_richness_plot <- birdlife_sample0_richness_summary[birdlife_sample0_richness_summary$model == 'full',]
birdlife_sample0_richness_plot$explanatory <- factor(birdlife_sample0_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlife_sample0_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlife_sample0_richness_plot$type[birdlife_sample0_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlife_sample0_richness_plot$type[birdlife_sample0_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlife_sample0_richness_plot$type[birdlife_sample0_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'
birdlifeSample0_plot = ggplot(birdlife_sample0_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) +
geom_line() +
geom_point()+
geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
position=position_dodge(0.05)) +
scale_y_discrete(
limits = rev(levels(birdlife_sample0_richness_plot$explanatory)),
labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
theme_bw() +
geom_vline(xintercept=0, linetype="dotted") +
guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
theme(legend.justification = "top")
birdlifeSample0_plot
}
plot_sample('0')
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample0.jpg')
Saving 7.29 x 4.51 in image

plot_sample('1')
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample1.jpg')
Saving 7.29 x 4.51 in image

plot_sample('2')
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample2.jpg')
Saving 7.29 x 4.51 in image

plot_sample('3')
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample3.jpg')
Saving 7.29 x 4.51 in image

plot_sample('4')
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample4.jpg')
Saving 7.29 x 4.51 in image

plot_sample('5')
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample5.jpg')
Saving 7.29 x 4.51 in image

plot_sample('6')
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample6.jpg')
Saving 7.29 x 4.51 in image

plot_sample('7')
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample7.jpg')
Saving 7.29 x 4.51 in image

plot_sample('8')
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample8.jpg')
Saving 7.29 x 4.51 in image

plot_sample('9')
Warning: comparing models fitted by REMLFixed term is "(Intercept)"
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample9.jpg')
Saving 7.29 x 4.51 in image

---
title: "Percentage of Regional Richness"
output: html_notebook
---

Run 'download_data.Rmd` First!

```{r setup}
options(na.action = "na.fail") 

library(tidyverse)
library(dplyr)

source("./helper__dredge_functions.R")
```

```{r}
model_average_richness <- function(data) {
  model_average(percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + urban + realm + number_of_checklists_scaled + (1|city_name), data, 4)
}
```

----------------------
Find city predictors
----------------------

```{r}
create_city_dataset <- function(mixed_model) {
  city_coeefs <- ranef(mixed_model)$city_name
  city_coeefs <- cbind(city_name = rownames(city_coeefs), city_coeefs)
  rownames(city_coeefs) <- NULL
  names(city_coeefs) <- c('name', 'response')
  
  left_join(city_coeefs, city_data)
}
```

----------------------
Merlin: Difference in richness
----------------------
```{r, cache = TRUE}
merlin_richness_result <- model_average_richness(merlin)
merlin_richness_result
```

```{r}
merlin_mixed_model <- lmer(percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1|city_name), merlin)
r.squaredGLMM(merlin_mixed_model)
```

```{r}
merlin_fixed_model <- lm(percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled, merlin)
anova(merlin_mixed_model, merlin_fixed_model)
```

```{r}
anova(merlin_mixed_model, lm(percentage_of_regional_pool_present ~ city_name, merlin))
```


```{r}
merlin_richness_sum <- model_summary('merlin', 'richness', merlin_richness_result)
merlin_richness_sum
```

```{r}
merlin_city_response <- create_city_dataset(merlin_mixed_model)
merlin_city_response[order(merlin_city_response$response), c('name', 'response')]
```

```{r}
write_csv(merlin_city_response[, c('name', 'response')], 'percentage_of_regional_richness__output__merlin_city_richness_intercept.csv')
```


```{r}
summary(merlin_mixed_model)
```

```{r}
merlin_richness_plot <- merlin_richness_sum[merlin_richness_sum$model == 'full',]
merlin_richness_plot$explanatory <- factor(merlin_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
merlin_richness_plot$type <- 'Proportion of landcover\n5 km around site'
merlin_richness_plot$type[merlin_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
merlin_richness_plot$type[merlin_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
merlin_richness_plot$type[merlin_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'

ggplot(merlin_richness_plot, aes(y=explanatory, x=richness_merlin_estimate, colour = type)) + 
  geom_line() +
  geom_point()+
  geom_errorbar(aes(xmin=richness_merlin_estimate-richness_merlin_error, xmax=richness_merlin_estimate+richness_merlin_error), width=.2,
                 position=position_dodge(0.05)) +
  scale_y_discrete(
    limits = rev(levels(merlin_richness_plot$explanatory)), 
    labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
  theme_bw() +
  geom_vline(xintercept=0, linetype="dotted") +
  guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional richness\n± Standard Error') + ylab('Predictor') +
  theme(legend.justification = "top")

ggsave('percentage_of_regional_pool__output__merlin_richness_result.jpg')
```

----------------------
Birdlife: Difference in richness
----------------------
```{r, cache = TRUE}
bl_richness_result <- model_average_richness(birdlife)
bl_richness_result
```

```{r}
birdlife_mixed_model <- lmer(percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + shrubs + realm + (1|city_name), birdlife)
r.squaredGLMM(birdlife_mixed_model)
```

```{r}
summary(birdlife_mixed_model)
```

```{r}
birdlife_fixed_model <- lm(percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + shrubs + realm, birdlife)
anova(birdlife_mixed_model, birdlife_fixed_model)
```

```{r}
summary(birdlife_fixed_model)
```

```{r}
birdlife_city_response <- create_city_dataset(birdlife_mixed_model)
birdlife_city_response[order(birdlife_city_response$response), c('name', 'response')]
```

```{r}
write_csv(birdlife_city_response[, c('name', 'response')], 'percentage_of_regional_richness__output__birdlife_city_richness_intercept.csv')
```


```{r}
bl_richness_sum <- model_summary('birdlife', 'richness', bl_richness_result)
bl_richness_sum
```

```{r}
summary(birdlife_mixed_model)
```

```{r}
birdlife_richness_plot <- bl_richness_sum[bl_richness_sum$model == 'full',]
birdlife_richness_plot$explanatory <- factor(birdlife_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlife_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlife_richness_plot$type[birdlife_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlife_richness_plot$type[birdlife_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlife_richness_plot$type[birdlife_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'

birdlife_plot = ggplot(birdlife_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) + 
  geom_line() +
  geom_point()+
  geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
                 position=position_dodge(0.05)) +
  scale_y_discrete(
    limits = rev(levels(birdlife_richness_plot$explanatory)), 
    labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
  theme_bw() +
  geom_vline(xintercept=0, linetype="dotted") +
  guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
  theme(legend.justification = "top")
birdlife_plot
ggsave('percentage_of_regional_pool__output__birdlife_richness_result.jpg')
```



----------------------
Both: Difference in richness
----------------------
```{r, cache = TRUE}
both_richness_result <- model_average_richness(both)
both_richness_result
```

```{r}
both_mixed_model <- lmer(percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1|city_name), both)
r.squaredGLMM(both_mixed_model)
```

```{r}
both_fixed_model <- lm(percentage_of_regional_pool_present ~ mean_elevation_scaled + elevation_delta_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled, both)
anova(both_mixed_model, both_fixed_model)
```

```{r}
both_richness_sum <- model_summary('both', 'richness', both_richness_result)
both_richness_sum
```

```{r}
both_city_response <- create_city_dataset(both_mixed_model)
both_city_response[order(both_city_response$response), c('name', 'response')]
```

```{r}
write_csv(both_city_response[, c('name', 'response')], 'percentage_of_regional_richness__output__both_city_richness_intercept.csv')
```

----------------------
Either: Difference in richness
----------------------
```{r, cache = TRUE}
either_richness_result <- model_average_richness(either)
either_richness_result
```

```{r}
either_mixed_model <- lmer(percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled + (1|city_name), either)
r.squaredGLMM(either_mixed_model)
```

```{r}
either_fixed_model <- lm(percentage_of_regional_pool_present ~ mean_elevation_scaled + closed_forest + cultivated + herbaceous_vegetation + herbaceous_wetland + open_forest + permanent_water + shrubs + realm + number_of_checklists_scaled, either)
anova(either_mixed_model, either_fixed_model)
```

```{r}
either_richness_sum <- model_summary('either', 'richness', either_richness_result)
either_richness_sum
```

```{r}
either_city_response <- create_city_dataset(either_mixed_model)
either_city_response[order(either_city_response$response), c('name', 'response')]
```

```{r}
write_csv(either_city_response[, c('name', 'response')], 'percentage_of_regional_richness__output__either_city_richness_intercept.csv')
```

----------------------
Full result
----------------------
```{r}
richness_all <- full_join(full_join(merlin_richness_sum, bl_richness_sum), full_join(both_richness_sum, either_richness_sum))
write_csv(richness_all, "percentage_of_regional_richness__output__richness_result.csv")
richness_all
```


---------------------------------------
Additional figures
---------------------------------------

```{r}
library(tidyr)
library(ggplot2)
library(ggpubr)
```

```{r}
environment <- merlin[,c('closed_forest', 'cultivated', 'herbaceous_vegetation', 'herbaceous_wetland', 'open_forest', 'permanent_water', 'shrubs', 'urban')]
names(environment) <- c('Closed Forest', 'Cultivated', 'Herbaceous Vegetation', 'Herbaceous Wetland', 'Open Forest', 'Permanent Water', 'Shurbs', 'Urban')
landcover <- gather(environment, landcover, proportion)

ggplot(landcover, aes(x = landcover, y = proportion)) + geom_boxplot() + xlab('Landcover Class') + ylab('Proportion in 5 km around sites') +
  scale_x_discrete(labels = scales::wrap_format(10)) + theme_bw() + labs(title = "Landcover Proportions")

ggsave("percentage_of_regional_richness__output__som_landcover_proportions.jpg")
```

```{r}
ggarrange(
  ggplot(merlin, aes(y = elevation_delta)) + geom_boxplot() + xlab('Elevation Delta') + ylab('Average Metres in 5 km around sites') +
    scale_x_discrete(labels = scales::wrap_format(10)) + theme_bw(),
  ggplot(merlin, aes(y = mean_elevation)) + geom_boxplot() + xlab('Mean Elevation') + ylab('Average Metres in 5 km around sites') +
    scale_x_discrete(labels = scales::wrap_format(10)) + theme_bw()
)

ggsave("percentage_of_regional_richness__output__som_elevations.jpg")
```


------------------------------------------------------------------
Birdlife [300 or less checklists]: Difference in richness
------------------------------------------------------------------
```{r}
birdlife300 = birdlife[birdlife$number_of_checklists <= 300,]
nrow(birdlife300)
```

```{r, cache = TRUE}
blr300_richness_result <- model_average_richness(birdlife300)
blr300_richness_result
```


```{r}
bl300_richness_sum <- model_summary('birdlife', 'richness', blr300_richness_result)
bl300_richness_sum
```

```{r}
birdlife300_richness_plot <- bl300_richness_sum[bl300_richness_sum$model == 'full',]

birdlife300_richness_plot$explanatory <- factor(birdlife300_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlife300_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlife300_richness_plot$type[birdlife300_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlife300_richness_plot$type[birdlife300_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlife300_richness_plot$type[birdlife300_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'

birdlife300_plot = ggplot(birdlife300_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) + 
  geom_line() +
  geom_point()+
  geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
                 position=position_dodge(0.05)) +
  scale_y_discrete(
    limits = rev(levels(birdlife300_richness_plot$explanatory)), 
    labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
  theme_bw() +
  geom_vline(xintercept=0, linetype="dotted") +
  guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
  theme(legend.justification = "top")
birdlife300_plot
ggsave('percentage_of_regional_richness__output__birdlife_richness_300_checklists_or_less_result.jpg')
```


------------------------------------------------------------------
Birdlife [No Australasia]: Difference in richness
------------------------------------------------------------------
```{r}
birdlifeNoAustralia = birdlife[birdlife$realm != 'Australasia',]
nrow(birdlifeNoAustralia)
```

```{r, cache = TRUE}
blrNoAustralia_richness_result <- model_average_richness(birdlifeNoAustralia)
blrNoAustralia_richness_result
```


```{r}
blNoAustralia_richness_sum <- model_summary('birdlife', 'richness', blrNoAustralia_richness_result)
blNoAustralia_richness_sum
```

```{r}
birdlifeNoAustralia_richness_plot <- blNoAustralia_richness_sum[blNoAustralia_richness_sum$model == 'full',]

birdlifeNoAustralia_richness_plot$explanatory <- factor(birdlifeNoAustralia_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlifeNoAustralia_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlifeNoAustralia_richness_plot$type[birdlifeNoAustralia_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlifeNoAustralia_richness_plot$type[birdlifeNoAustralia_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlifeNoAustralia_richness_plot$type[birdlifeNoAustralia_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'

birdlifeNoAustralia_plot = ggplot(birdlifeNoAustralia_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) + 
  geom_line() +
  geom_point()+
  geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
                 position=position_dodge(0.05)) +
  scale_y_discrete(
    limits = rev(levels(birdlifeNoAustralia_richness_plot$explanatory)), 
    labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
  theme_bw() +
  geom_vline(xintercept=0, linetype="dotted") +
  guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
  theme(legend.justification = "top")
birdlifeNoAustralia_plot
ggsave('percentage_of_regional_richness__output__birdlife_richness_no_australia.jpg')
```

------------------------------------------------------------------
Birdlife [No Afrotropics]: Difference in richness
------------------------------------------------------------------
```{r}
birdlifeNoAfrica = birdlife[birdlife$realm != 'Afrotropic',]
nrow(birdlifeNoAfrica)
```

```{r, cache = TRUE}
blrNoAfrica_richness_result <- model_average_richness(birdlifeNoAfrica)
blrNoAfrica_richness_result
```

```{r}
blNoAfrica_richness_sum = model_summary('birdlife', 'richness', blrNoAfrica_richness_result)
```

```{r}
birdlifeNoAfrica_richness_plot <- blNoAfrica_richness_sum[blNoAfrica_richness_sum$model == 'full',]

birdlifeNoAfrica_richness_plot$explanatory <- factor(birdlifeNoAfrica_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlifeNoAfrica_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlifeNoAfrica_richness_plot$type[birdlifeNoAfrica_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlifeNoAfrica_richness_plot$type[birdlifeNoAfrica_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlifeNoAfrica_richness_plot$type[birdlifeNoAfrica_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'

birdlifeNoAfrica_plot = ggplot(birdlifeNoAfrica_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) + 
  geom_line() +
  geom_point()+
  geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
                 position=position_dodge(0.05)) +
  scale_y_discrete(
    limits = rev(levels(birdlifeNoAfrica_richness_plot$explanatory)), 
    labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
  theme_bw() +
  geom_vline(xintercept=0, linetype="dotted") +
  guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
  theme(legend.justification = "top")
birdlifeNoAfrica_plot
ggsave('percentage_of_regional_richness__output__birdlife_richness_no_africa.jpg')
```

------------------------------------------------------------------
Birdlife [No Afrotropics and no Australisia]: Difference in richness
------------------------------------------------------------------
```{r}
birdlifeNeitherAfrNorAus = birdlife[birdlife$realm != 'Afrotropic' & birdlife$realm != 'Australasia',]
nrow(birdlifeNeitherAfrNorAus)
```

```{r, cache = TRUE}
blrNAA_richness_result <- model_average_richness(birdlifeNeitherAfrNorAus)
blrNAA_richness_result
```


```{r}
blNAA_richness_sum <- model_summary('birdlife', 'richness', blrNAA_richness_result)
blNAA_richness_sum
```

```{r}
birdlifeNAA_richness_plot <- blNAA_richness_sum[blNAA_richness_sum$model == 'full',]

birdlifeNAA_richness_plot$explanatory <- factor(birdlifeNAA_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlifeNAA_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlifeNAA_richness_plot$type[birdlifeNAA_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlifeNAA_richness_plot$type[birdlifeNAA_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlifeNAA_richness_plot$type[birdlifeNAA_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'

birdlifeNAA_plot = ggplot(birdlifeNAA_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) + 
  geom_line() +
  geom_point()+
  geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
                 position=position_dodge(0.05)) +
  scale_y_discrete(
    limits = rev(levels(birdlifeNAA_richness_plot$explanatory)), 
    labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
  theme_bw() +
  geom_vline(xintercept=0, linetype="dotted") +
  guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
  theme(legend.justification = "top")
birdlifeNAA_plot
ggsave('percentage_of_regional_richness__output__birdlife_richness_no_africa_nor_australisia.jpg')
```


------------------------------------------------------------------
Birdlife [Urban Only]: Difference in richness
------------------------------------------------------------------
```{r}
birdlife$urban_rounded = floor(birdlife$urban * 10) / 10
ggplot(birdlife, aes(x = urban_rounded)) + geom_bar()
```

```{r}
birdlifeHigherUrban = birdlife[birdlife$urban > 0.2,]
nrow(birdlifeHigherUrban)
```

```{r, cache = TRUE}
blrHigherUrban_richness_result <- model_average_richness(birdlifeHigherUrban)
blrHigherUrban_richness_result
```


```{r}
blrHigherUrban_richness_sum <- model_summary('birdlife', 'richness', blrHigherUrban_richness_result)
blrHigherUrban_richness_sum
```

```{r}
birdlifeHR_richness_plot <- blrHigherUrban_richness_sum[blrHigherUrban_richness_sum$model == 'full',]

birdlifeHR_richness_plot$explanatory <- factor(birdlifeHR_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
birdlifeHR_richness_plot$type <- 'Proportion of landcover\n5 km around site'
birdlifeHR_richness_plot$type[birdlifeHR_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
birdlifeHR_richness_plot$type[birdlifeHR_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
birdlifeHR_richness_plot$type[birdlifeHR_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'

birdlifeHR_plot = ggplot(birdlifeHR_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) + 
  geom_line() +
  geom_point()+
  geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
                 position=position_dodge(0.05)) +
  scale_y_discrete(
    limits = rev(levels(birdlifeHR_richness_plot$explanatory)), 
    labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
  theme_bw() +
  geom_vline(xintercept=0, linetype="dotted") +
  guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
  theme(legend.justification = "top")
birdlifeHR_plot
ggsave('percentage_of_regional_richness__output__birdlife_richness_higher_urban.jpg')
```

------------------------------------------------------------------
Birdlife [Check pseudoreplication]: Difference in richness
------------------------------------------------------------------

```{r}
locality_samples = read_csv('percentage_of_regional_richness__input__sampled_localities_min_distance_500m.csv')
table(locality_samples$SAMPLE_ID)
```


```{r}
plot_sample = function(sample_id) {
  birdlife_sample0 = birdlife[birdlife$locality_id %in% locality_samples$LOCALITY_ID[locality_samples$SAMPLE_ID == sample_id],]
  
  birdlife_sample0_richness_result <- model_average_richness(birdlife_sample0)
  birdlife_sample0_richness_summary <- model_summary('birdlife', 'richness', birdlife_sample0_richness_result)
  
  birdlife_sample0_richness_plot <- birdlife_sample0_richness_summary[birdlife_sample0_richness_summary$model == 'full',]
  
  birdlife_sample0_richness_plot$explanatory <- factor(birdlife_sample0_richness_plot$explanatory, levels = c('closed_forest', 'open_forest', 'shrubs', 'herbaceous_vegetation', 'herbaceous_wetland', 'permanent_water', 'cultivated', 'urban', 'mean_elevation_scaled', 'elevation_delta_scaled', 'realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic', 'number_of_checklists_scaled'))
  birdlife_sample0_richness_plot$type <- 'Proportion of landcover\n5 km around site'
  birdlife_sample0_richness_plot$type[birdlife_sample0_richness_plot$explanatory %in% c('mean_elevation_scaled', 'elevation_delta_scaled')] <- 'Scaled (0-1) average\nelevation 5 km around site'
  birdlife_sample0_richness_plot$type[birdlife_sample0_richness_plot$explanatory %in% c('realmAfrotropic', 'realmAustralasia', 'realmIndomalayan', 'realmNearctic', 'realmNeotropic')] <- 'Realm'
  birdlife_sample0_richness_plot$type[birdlife_sample0_richness_plot$explanatory %in% c('number_of_checklists_scaled')] <- 'Scaled (0 - 1) number\nof checklists'
  
  birdlifeSample0_plot = ggplot(birdlife_sample0_richness_plot, aes(y=explanatory, x=richness_birdlife_estimate, colour = type)) + 
    geom_line() +
    geom_point()+
    geom_errorbar(aes(xmin=richness_birdlife_estimate-richness_birdlife_error, xmax=richness_birdlife_estimate+richness_birdlife_error), width=.2,
                   position=position_dodge(0.05)) +
    scale_y_discrete(
      limits = rev(levels(birdlife_sample0_richness_plot$explanatory)), 
      labels = c('closed_forest' = 'Closed forest', 'open_forest' = 'Open forest', 'shrubs' = 'Shrubs', 'herbaceous_vegetation' = 'Herbaceous\nvegetation', 'herbaceous_wetland' = 'Herbaceous\nwetland', 'permanent_water' = 'Permanent\nwater', 'cultivated' = 'Cultivated', 'urban' = 'Urban', 'mean_elevation_scaled' = 'Mean elevation\nscaled', 'elevation_delta_scaled' = 'Elevation delta\nscaled', 'realmAfrotropic' = 'Afrotropical', 'realmAustralasia' = 'Austaliasian', 'realmIndomalayan' = 'Indomalayan', 'realmNearctic' = 'Nearctic', 'realmNeotropic' = 'Neotropical', 'number_of_checklists_scaled' = 'Sqrt number\nof checklists')) +
    theme_bw() +
    geom_vline(xintercept=0, linetype="dotted") +
    guides(colour=guide_legend(title="Predictor type")) + xlab('Increase in proportion of regional pool richness\n± Standard Error') + ylab('Predictor') +
    theme(legend.justification = "top")
  birdlifeSample0_plot
}
```

```{r}
plot_sample('0')
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample0.jpg')
```

```{r}
plot_sample('1')
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample1.jpg')
```


```{r}
plot_sample('2')
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample2.jpg')
```

```{r}
plot_sample('3')
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample3.jpg')
```

```{r}
plot_sample('4')
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample4.jpg')
```

```{r}
plot_sample('5')
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample5.jpg')
```

```{r}
plot_sample('6')
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample6.jpg')
```

```{r}
plot_sample('7')
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample7.jpg')
```

```{r}
plot_sample('8')
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample8.jpg')
```

```{r}
plot_sample('9')
ggsave('percentage_of_regional_richness__output__birdlife_locality_sample9.jpg')
```